
and where \(f\left(\theta _i\right)\)  are examples of the true targets obtained from the compressed training data using the autoencoder introduced in the previous sections. Gradients of this cost function can then be used in an optimization framework to obtain the best weights and biases, given the training data. Finally, the trained MLP may be used for uniformly approximating any continuous function on compact domains [1]}, [2]}, provided \(\eta (x)\)  is not polynomial in nature.
